Facebook Inches Closer to Figuring Out the Formula for Love

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Facebook Inches Closer to Figuring Out the Formula for Love

Photo: Ariel Zambelich/WIRED

Facebook can do a lot of things—it can serve up tailored ads, recommend new friends and even use facial recognition to pinpoint who’s in our photos—but the social media site is not a romantic soothsayer. At least, not yet. But you might think so if you’ve read the internet lately, which has has been abuzz with a new study that shows how an algorithm can accurately guess a user’s romantic partner or foretell a potential breakup based on the structure of his or her social network. All this is true—sort of. (We’ll get to that in a bit). More than anything, the algorithms and how they work demonstrate how Facebook is inching closer to producing predictive, even counterintuitive insights about our lives.

The study comes out of Cornell University, from computer scientist Jon Kleinberg and his former thesis student Lars Backstrom, who is currently at Facebook as a senior engineer working on improving our news feeds. Boiled down, the researchers developed a new way to measure the quality of our relationships on Facebook.

For a long time, social scientists have believed that embeddedness—the number of mutual friends you share with someone—is the best indicator of how close you are to that person, romantically or otherwise. That seems to make a certain intuitive sense: After all, if you know a lot of people in common, aren't you more likely to know someone well? Not so fast. As Kleinberg and Backstrom learned, a more accurate barometer of a relationship status is not how many people you have in common, it’s what kinds of friends you have in common.

This idea, dubbed "dispersion" by the researchers, is a measure of how many overlapping social circles a friend touches in your network. An easy way to think of it is this: Pick out a colleague and you’ll likely have dozens of friends in common with that person on Facebook, the majority of whom also work with you. You share a lot of people in common, but is this person your closest friend? Probably not. Rather, the people you're closest to likely share friends who span across your different social spheres—work, school and family—regardless of the total number of mutual friends. The degree to which you share friends across many spheres yields far better predictions about the nature of your real-world relationships.

>The people you're closest to likely share friends who span across your different social spheres.

By analyzing social networks with the dispersion algorithm rather than embeddedness, Kleinberg and Backstrom were able to accurately guess a user’s spouse correctly 60 percent of the time and a non-marital romantic partner nearly 50 percent of the time. Those are pretty impressive numbers, given that the data set comprised 1.3 million Facebook random users who were at least 20 years old, had between 50 and 2,000 friends and noted some form or relationship status on their profile. (The odds of guessing a randomly guessing a partner would thus range between 1-in-50 to 1-in-200—or between 30 and 120 times less than the results achieved by Kleinberg and Backstrom.)

The process involved running roughly 379 million nodes and 8.6 billion links through an algorithm that essentially turned them into pawns in a very smart game of Guess Who. “It was like a quiz,” says Kleinberg. “We’d say yep, that’s right, no that’s wrong.” That analysis soon yielded some surprising results. When you break the findings into non-marital relationships vs. spouses, it turns out that the best mode for determining the correct partner totally differs. Using network structure, or dispersion, was by far a superior method for guessing a spouse, whereas in non-marital relationships, activity-based features like profile viewing, messaging and liking were far more telling. “At first when the relationship is extremely new, like a month old, profile views are a great way to find a relationship partner,” Kleinberg says. “People are viewing each other’s profiles a lot.” As relationships progressed, the network became a better indicator once again, which it actually a pretty accurate reflection on relationships themselves. As we grow closer to someone, so do our social networks. By the time you're married, it's likely that you and your spouse's social networks are heavily entangled.

This is the structure of a social network. The two people romantically involved are the person in the center, and the lone point in the lower lefthand area.

Image: Facebook

But perhaps the most fascinating idea within the research came when you flip this formulation around. What happens to relationships when people's social networks aren't many-tentacled? Turns out, in cases where there was low dispersion (where the couple had a lot of mutual friends, but from distinct social circles), couples were 50 percent more likely to change their status to "single." Put another way, having social networks that mirror each other too closely in one particular part of your life seems to result in more transitory romances. There's a common sense way of reframing this idea: After all, how many people have you broken up with right around the time that it became clear that your friend circles weren't gelling?

>In cases where there was low dispersion, couples were 50 percent more likely to change their status to 'single.'

Facebook isn't angling to be the next OK Cupid. Though Backstrom wouldn't confirm how (or if) it might be implemented at Facebook, the algorithm is really meant to bolster the social media site's ability to serve its users more relevant information based on their relationships—for example, strengthening the news feed or making those ridiculously wrong ads just a little more right. But the fact is, the social media site already has much of the information it needs to be an extremely proficient matchmaker—it knows our habits, our friends, our interests, who we’re interested *in *(yes, Facebook knows you clicked on that profile five times last night).

Which leads us to the real question: How long until Facebook can accurately predict who we might be in a relationship with next?

That's not something Backstrom was willing to comment on, but Kleinberg says that to get to a point where "People You May Know" is replaced with "People You Should Date," it's going to take a whole new harnessing of information, tying in both in-network and out-of-network information. Dispersion is a start, sure, but as Kleinberg notes, it’s only a piece of a bigger algorithmic puzzle. “It’s not just about who you’re linked to, but your activity like messaging, viewing of profile, communication,” he says. "All of that is going to be ingredients in trying to think about these questions.”